There are many applications for analyzing tissue samples that are stained with multiple biomarkers. How the same tissue reacts to staining by different biomarkers is determined by slicing the tissue into multiple very thin slices and then separately staining the slices. In order to determine how different biomarkers stain the same tissue structures, however, digital images of the slices must be coregistered to indicate which tissue structures in one slice correspond to similar tissue structures in the other slices. Coregistration of the digital images is possible only if the thickness of the slices is thin enough so that cross sections of the same tissue structures appear in the digital images of multiple slices. For example, multiple slices may pass through a single cancerous region of tissue, and it may be possible to determine that the various outlines of the cancerous region correspond to the same cancerous region in another image even though the outlines are not identical. Coregistration of the digital images involves mapping of pixels from the digital image of one slice to the related pixels of the digital image of an adjacent slice or nearby slice.
A particular complication arises when the digital images to be coregistered include cross sections of multiple needle core biopsy samples as opposed to a continuous slice of tissue. As the slices of the multiple paraffin-embedded needle biopsy samples are cut, the elongated needle tissue samples bend, shift, stretch and become generally distorted. Most importantly, different slices of the same needle tissue sample are distorted differently. So the task of coregistering the digital images of adjacent or nearby needle tissue samples requires much more than merely determining the x-y translation and rotation of one image with respect to the other image. Because the tissue slices are distorted, each pixel of one image must be separately coregistered to the appropriate pixel in the other image.
One method of separately coregistering each pixel of one image with the associated pixel of the other image is to segment, classify and identify sufficiently small corresponding tissue structures throughout each of the digital images. The pixels are coregistered by determining the correspondence between tissue structures in the images. However, determining corresponding low-order tissue structures, such as cells, is computationally intensive at full resolution because digital images of tissue slices typically have a very high spectral resolution, which can be on the order of several Giga-pixels. In addition, these low-order tissue structures are not consistent from slice to slice; a cell in one slice does not necessarily appear in another slice. Performing segmentation on all of the structures in the images of multiple slices and then comparing each low-order structure in each image to all of the low-order structures in the other images to find corresponding low-order structures is typically not computationally feasible.
One solution for reducing computation time and improving consistency of the structures that are used for coregistration is to perform segmentation on low-resolution superimages of the tissue slices. Higher-order structures are then used for the coregistration, such as stromal-tissue regions, fatty-tissue regions, cancerous regions or connective-tissue regions. The structures of highest order in the digital images are the tissue samples themselves, whose outlines can be used for coregistration. But coregistration performed using higher-order structures is consequently imprecise because of their possible distortion from slice to slice.
A more precise method of coregistering images of needle biopsy tissue samples is sought that does not require the segmentation of all of the tissue structures in the various tissue slices. Moreover, a method is sought for displaying the various different staining results that simultaneously depicts corresponding structures in the various digital images of differently stained tissue.